Machine Learning Driven Optimisation of Energy Efficiency and Indoor Air Quality in Educational Buildings
Educational buildings breathe life into growing minds, yet they often struggle to balance two essential needs: conserving energy and maintaining healthy indoor air quality. The study behind this work steps into that tension, exploring how traditional HVAC control methods fall short when faced with shifting indoor conditions and rising sustainability pressures. By pairing experimental testing with advanced machine learning models, the research aims to create smarter, adaptive systems that reduce energy consumption while protecting occupant health. This introduction frames the need for innovation, outlining why educational spaces demand data-driven solutions capable of reacting in real time. Machine Learning Integration for HVAC Optimization The research explores how models such as RNN, LSTM, GRU, and CNN can interpret vast streams of environmental data and turn them into actionable predictions. With over 35,000 real-world records, the study demonstrates how these models learn ...